* what is evolution? Examples of what evolution is and is not, experimental and theoretical approaches. * interactions between genes (epistasis), synthetic/suppressive effects, and gene action (example of processes that set up evolution). * graphs and evolutionary representations (example of theory).

Genes and Systems

Genes and Systems - Introduction Goal of this section:

To learn about evolution and biology as a complex system.

* introduction to biological methods, concepts, and current issues.

* three lectures: Genes and Systems, Organismal Self-organization I, and

Organismal Self-organization II.

Evolutionary systems biology compendium:

* please read through and keep as a reference document (bibilography of selected

topics, schedule for lectures).

Homework:

* phylogenetic and population genetics exercise.

Genes and Systems - Introduction Evolution not “survival of the fittest”, but “descent with modification”:

Evolution is an adaptive process, but stochastic (random process).

* evolution is not always optimal.

* involves mutation (changes in gene sequences), recombination of genetic material

during sexual reproduction, the flow of genes in a population, and the restriction of

genes - reproductive isolation - in a population (drift).

Evolution is an unfolding process (over multiple generations).

* however, the signature of evolution is apparent in every individual (variation,

biodiversity).

* evolution involves a process called natural selection, which results in differential

reproduction.

* evolution also involves neutral processes, which are driven by population

processes.

Genes and Systems - Introduction Why do biologists use trees to

describe the diversity of life?

Tree of Life: http://www.tolweb.org

* lineage sorting (species diverge from

one another, lineages remain separate).

IN GENERAL – a tree is a useful way to

characterize biodiversity.

BUT – there are several processes that

violate this assumption:

* within-species sexual reproduction,

lateral gene transfer, cultural evolution,

hybridization.

* nevertheless, we still have a directed network to work with (heuristic for capturing

structure of biodiversity).

Ideas that define modern evolutionary theory

Darwin: introduces the idea of natural selection

(or descent with modification).

Mendel: introduces the idea of independent

assortment (single copy of a gene is inherited

from either parent).

Wright, Haldane, Fisher, Morgan: introduces

methods for population and experimental genetics.

E.O. Wilson, Richard Dawkins: extended

evolutionary ideas to the realm of groups

and environments.

Sean Carroll, Brian K. Hall: evolution of development

approach (builds on earlier work of Gould and

embryologists).

AVIDA/artificial life: digital organisms that behave

and replicate like bacterial populations

(http://devolab.cse.msu.edu).

Evolution: an experimental approach Experimental Evolution: Observing evolution directly is hard!

Most of evolutionary biology is done via inference (gene sequences, fossils).

Problems with experimental approach:

* generation times (mostly too long to observe).

* until recently, control of genes and expression.

Successful experimental endeavors:

Rich Lenski (genes in E. coli): > 50K generations!

Theodore Garland (physiological traits in mice)

Digital Evolution Group (evolution of complex

traits – AVIDA)

Andrew Murray (transgene vs. control - yeast)

Tadeusz Kawecki (learning and memory in

Drosophila – fruit fly)

Evolution: an experimental approach (con’t)

Artificial Selection:

The other need for experimental work is an adequate form of selection. Impose

selection in lab or via animal husbandry.

Examples:

Selective breeding of wild type animals (dog domestication, fruit flies): results in

fixation of phenotypic/behavioral traits, but much hitchhiking of other genes

(unintended consequences).

* nutrients in a bacterial medium: changing nutrient

conditions in vivo.

* in AVIDA, selection coefficient is set by metabolic

conditions; more successful genotypes consume

greater number of resources.

* selective breeding of knockout organisms:

breeding genetically altered strains (fly, mouse, yeast).

Evolution: a theoretical approach Neutral evolution (or “survival of the

good enough”):

* evolutionary changes that don’t result from

natural selection (no selective advantage per

se).

* neutral drift example: changes in gene

frequencies resulting from a restriction of

gene pool (e.g. reproductive isolation).

What is a neutral space?

A space of all possible genotypes and the number of mutational steps needed to get

there (genotypes = gene sequences).

When selection (variable intensity) is applied, anywhere from one to n possible

outcomes (weak selection, many possible outcomes, strong selection, only one path

to a “fit enough” genotype).

Evolution: a theoretical approach (con’t)

* Archaea (microbial organisms, live in extreme environments).

* Eubacteria (microbial organisms like bacteria).

* Eukaryota (yeast, plants, and animals).

Both archaea and eubacteria are

prokaryotic, which is defined

by a cell type and level of

complexity.

What do we do when our

concept of “life” is altered?

In 1971, Carl Woese proposed that three

(rather than two) domains of “life” exist.

* a reclassification based on new

evidence.

* not all new evidence warrants a change

in classification (e.g. sequenced

genomes).

What is epistasis? Epistasis is the interactions between genes:

* some traits monogenic (sickle cell anemia, hemophilia). Basic Hardy-Weinberg (H-W).

* many traits multigenic (multiple genes, copy number variation). Epistasis plays role.

Genes interact with each other during expression:

* have suppressive, additive, and synergistic effects

(not gene action).

* many mechanisms of interaction (posttranscriptional

and posttranslational).

* interact in a network; one gene might regulate another

through 1) facilitating or 2) inhibiting activity.

* epistasis does not involve environment, may indirectly

affect phenotypic expression.

What is epistasis? (con’t)

Heredity and Gene Action (diploid genome):

* gene action: copies of a gene combine, express trait (physical, biochemical, behaviors).

* alternate forms of locus = alleles. Alleles take two forms: dominant (A), recessive (a).

* heterozygote (Aa) = beneficial, homozygous recessive (aa) = deleterious.

Monogenic trait:

* one parent has genotype Aa, other has

genotype Aa. Parents = F1

* law of independent assortment: 3

combinations of genotypes in offspring

(F2): AA, Aa, aa, varying frequencies.

* in this case, allele A = dominant. AA

and Aa = outcome in phenotype. Only

aa = recessive phenotype.

A (dominant) a (recessive)

A (dominant) AA Aa

a (recessive) Aa aa

Processes that set up Evolution 2) How can gene action affect the phenotype

post-translationally?

Matzke and Matzke, PLoS Biology, 2(5), 2004:

Basic mechanism of microRNAs (at right).

* short (<100bp) RNA sequences bind up/

degrade viruses, extra transcripts.

Petunia Example: create “more purple” petunia.

* add a transgene (third allele).

* if purple = dominant allele, then

third purple allele = more purple.

Flowers turned out differently. Why?

* violation of gene action, normal copy number variation: short interfering RNAs

silences both enhanced and normal expression.

Graphs and evolutionary representations

Coalescent Theory:

* gene geneaologies (tree-like structure).

* upside-down statistical gene-based phylogeny (each branch = # of changes).

Method:

* select a specific gene or locus.

* sample from population (N).

* infer relationships between taxa (tips of tree) statistically.

Q: in each previous generation [T(2),T(3)],

what is the likelihood of two lineages converging (see frame B of figure)?

Graphs and evolutionary representations (con’t)

Coalescent graph, branches based on union

of different sequence n generations in past:

Calculate parameters: * Ne = initial population size (theoretical minimal pop. size

required for breeding).

* μ =mutation rate (change per unit time).

* θ = 4(Ne)μ (parents are diploid, measure of variation).

Determine coalescence:

* Pc(t) = [1 – 1 / 2N] t-1 * [1 / 2N]

[1 – 1 / 2N] t-1 = prob. lineage DOES NOT coalesce at t-1.

[1 / 2N] = prob. lineage DOES coalesce at t – 0.

Graphs and evolutionary representations (con’t)

Four-taxon case: line between x and y is root, or origin point of the graph.

Lines to A, B, O, and C represent “branches” to individual taxa.

* length of branches ~ taxon has undergone n amount of evolution (each step/length of

branch = number of mutations, character state changes).

* x and y represent ancestral species (branching points between two related species).

* often used to test search algorithms.

QUIZ: in tree #1, which taxa are closer

to ancestral state? They are equidistant.

In tree #2, which taxa are closer to

the ancestral state? O and C.

Other Data Structures for Natural Variation

ENCODE project browser (http://genome.ucsc.edu/ENCODE/):

Project started to understand the functional significance of genomic elements.

* many genomes have been sequenced

(plants, animals, bacteria).

* human genome sequenced in 2001.

ENCODE project tried to distinguish “biochemical function” from “biological

role” (Nature, 447, 799-816, 2007). Used multiple sampling methods and

annontation (bioinformatics).

* 1% of human genome (survey).

* IDed novel non-protein coding transcripts (some formerly thought of as “junk” DNA).

* “junk” DNA not junk – diverse set of genetic elements, some serve no purpose.

* abundance of transcriptional start sites (epigenetic mechanisms – activity of start sites).

The myth of “Junk” DNA? Yes and no.

Food for thought (until next time): what is the function of our genome?

Historically, “junk” DNA was used to describe all non-coding sequences.

* recently, an increasing amount of this “junk” has been found to have

regulatory functions (although much also ~ transcriptional noise).

Genome size = # of nucleotide bases.

# of genes in genome = number of sequences

that code for proteins.

No correleation between organismal complexity and genome size or function.

However, much of the formerly defined “junk” have a regulatory function,

particularly “jumping genes” (transposons).

Organismal Self-organization I

* myth of “junk” DNA (genome content and structure). * gene networks and biocomplexity (genome function). * synthetic life and bioengineering (relevance of evolutionary systems biology to regenerative medicine and systems engineering).

The myth of “Junk” DNA? Yes and no (con’t).

Biemont and Vieira (2006). Nature, 443, 521-524.

Transposable elements (TEs): “jumping genes”

(translocate from one location to another).

* can disrupt a gene’s function during transcription.

* can significantly influence gene regulation.

* can contribute to the appearance of mutations,

which can result in disease.

* TEs are under epigenetic control, may contribute

to cancers and other diseases (not mutational!).

Transposable elements (and genome size) can also be influenced by population

processes (historically small, large population size).

* population bottlenecks (neutral processes) can affect # transposons in genome.

Example of “yes and no”: Retrotransposons

Retrotransposons: retroviral (e.g. HIV) elements, insert into host genome, may

even become heritable (exact mechanisms are controversial).

* HIV virus is a retrotransposon. Inserts itself into genome at active transcription site and uses machinery

to make copies of itself.

* retrotransposons violate the central dogma, which states that genomic information unfolds by being

transferred from genes to RNA to proteins.

* retrotransposons are frequently used for introducing genes in creation of transgenic cell lines. The

delivery of genes to cells is often done using these viral elements.

In situ retrotransposons: humans inherit a significant number of ancient

retrotransposons (just like genes) from mother and father, have been in genome for

generations.

* mostly non-functional, but may contribute to disease phenotypes (if actively transcribed).

* underscores the complexity of the genome, but just one example (for another example, look up Jonathan

Widom’s work on the nucleosome code).

Gene Networks and Biocomplexity

From Lecture #1:

Gene action is dependent on many genetic elements interacting.

* how do we characterize these interactions computationally?

* how do we recreate the higher-order interactions (e.g. rate-limiting, saturation,

feedbacks)?

Genetic regulatory networks (GRNs):

* gene regulation is the key to this model. Instead of a locus with multiple alleles,

the unit of analysis is the operon (regulatory elements, coding sequences).

* stoichiometry is another quality (a certain amount of gene product expressed,

degraded, contributes to function).

Gene Networks and Biocomplexity (con’t)

In silico GRN components (see lower left):

* regulatory interactions (set W).

* i is a specific operon (single gene and

regulatory elements such as activators,

repressors, etc).

* j is a specific element within the operon.

* S is the phenotype, in this case a gene

product.

* Sq is the gene product quantity.

Problems:

1) is this experimentally tractable? Does it map

to biologically-realistic functions?

2) What kinds of outputs does the network

produce? Sufficiently complex? Robust when

gene function is altered?

Jacob and Monod (discovered operon

using bacteria) – 1960s: complex

networks of genes interact to regulate

cell differentiation.

Stu Kauffman (1960s): interactions

understood using computational tools

(simple epistasis not enough).

Gene Networks and Biocomplexity (con’t)

Genetic regulatory networks in vivo: Britten/Davidson model (1973)

Inducer: small molecule, large number of enzymes, external signals producing a

pleiotropic (one gene, many effects) response.

cis-element: regulatory element in same operon as gene it regulates.

trans-element: regulatory element located in different operon as gene it regulates.

Sensor Gene: inducer binds to promoter.

Integrator Gene Set: gene being regulated.

Activator RNA: translational agent.

Receptor: structural gene site, activators bind

receptors in combinatoric fashion.

From Latchman, D.S. Gene

Regulation. Routledge.

Gene Networks and Biocomplexity (con’t)

Example: Espinosa-Soto et.al, The Plant Cell, 16, 2923–2939 (2004).

* constructed a GRN using experimental

data for floral organ formation in

Arabidopsis thaliana.

* used a dynamical network model (see

Kaufmann, 1993) to obtain attractor

points as output.

* epistasis is multiplicative (large # of

potential states) – but small # of

observed morphologies (conserved

but robust).

* model reduces large # of states to

tractable # of active states, corresponds

with transcriptional profiles.

Actual genes = nodes.

Interactions are inferred from

literature.

Logical rules govern

interactions between nodes

(activity, timing).

Plant morphologies match

active network states.

Synthetic Life and Bioengineering

What can we do with an understanding of genomic complexity? Bioengineering

applications.

What is Bioengineering?

* the harnessing of life to make useful things by modifying the structure of function

of the organism.

http://2008.igem.org

http://openwetware.org/wiki/Synthetic_Biology:BioBricks

* focus on microbial organisms and cellular models (for now).

Example: Craig Venter’s group engineered the genome of a microbe to perform

specific functional tasks (clean up toxic waste, medical applications).

* Student groups have also worked on projects involving things like programmed

bioluminescence (open-source biology).

Synthetic Life and Bioengineering (con’t)

Drew Endy approach to procedures/goals of synthetic biology (technique for apps e.g.

metabolic engineering, nanotech):

Traditional tools:

* recombinant DNA (create

sequences).

* PCR (amplify genetic material).

* automated sequencing (read

genetic material).

These define basic read/write

operations.

More advanced tools:

* automated construction (making things

out of DNA, proteins, secondary structure).

* standards (list of biological parts).

* abstraction (encode and compress

biological complexity).

Goal is to create a programming language

for self-assembly, creation of complex

objects.

Synthetic Life and Bioengineering (con’t)

How to design a customized microbial genome:

* microbes have a haploid, circular genome (right),

a few 1000 genes.

Combinatorial knockout experiments:

* brute force, knockout every gene, see if microbe dies.

If so, gene is essential. If not gene can be knocked out.

* result is skeletal genome with only essential genes

(necessary for survival). Applications come from “booting

up” microbes with genes only for specialized functions.

* works well in microbes, not a great approach for

plant or animal genomes.

Synthetic Life and Bioengineering (con’t)

Question: how does cellular differentiation occur in

nature and across phylogeny?

In C. elegans, cell fate maps have been worked out

(developmental model):

* cell at certain position in body (gut, head) will become specific cell type.

* differentiate during development

* good model for understanding links between structure and function.

Cell fate (undifferentiated cells): * local paracrine factors.

* gene expression gradients.

* fate of local population.

Synthetic Life and Bioengineering (con’t)

In vertebrates: cell fate can be more complex:

* in some systems (weakly electric fish tail repair)

localized dedifferentiation occurs naturally.

* transplanted stem/iPS cells take on fate of local

population.

* example: cardiac muscle.

* other processes (such as neural repair) not well

understood – may involve production of new

pluripotent cells in adulthood.

* in these cases, a “cell fate map” makes less

sense (differentiation indispensible part of life

cycle).

Story of Cellular Reprogramming Q: how do we create customized

stem cells for therapy and disease

research?

A: create iPS (or induced

pluripotent stem) cells.

Pluripotent cells: capable of

differentiating into any cell type.

* four transcription factors (Oct4,

Sox2, c-Myc, and Nanog) can be

used to “reprogram” differentiated

cells to pluripotent (stem-cell like).

* cells can be used as delivery

system for gene therapy, or as way

to repair damaged tissue (integrate

into cell population).

Shinya Yamanaka: discovered

four factor trigger using a

“high-throughput” genetic

screen.

Story of cellular reprogramming (con’t) Anatomy of a genetic circuit: Oct4, Sox2 artificially stimulated by transgenic

element.

* NANOG both triggered by Oct4/Sox2 and stimulates further expression (first-order

positive FB).

* presence of all three suppress differentiation genes and activate stem cell genes

(modules).

Hypothesis: Reprogramming is a critical process.

“Cellular-wide avalanche” occurs at level of gene

regulation (e.g. large-scale downregulation of

differentiation genes).

* critical process: one event (small magnitude) can trigger many large

events. Leaves a power-law signature (1/f noise).

* noise can trigger, drive reprogramming process in culture dish. In

general., external stimuli can facilitate reprogramming.

* perspective lacking from traditional biomedical approaches.

Story of cellular reprogramming (con’t)

Sandpile model is a metaphor for the

reprogramming process (several

avalanches occur during

reprogramming). See next slide.

Sandpile model interactive demo:

http://www.cmth.phy.bnl.gov/~maslov/sandpile.htm

Noise may facilitate reprogramming process:

Computational approaches to gene expression include adding

noise (stochastic element) to model.

* non-specific noise in expression of four factors, other genes can trigger

reprogramming.

Black function: Oct4, Sox2. Blue function: NANOG. Red function: lineage-

specific master genes, σ: parameter value for amplitude of noise (same for

every genes).

Up next: “Reducible Complexity”

Evolvability: the capacity of an organism to evolve

traits. How easy is it to evolve a trait give the prior

state of the organism (OR why didn’t humans grow

horns, and why did rams)?

* historical contingency. (path-dependence – does one trait depend

on another?). Assumes common ancestry.

* hopeful monster (complex traits arise de novo). “X-Men” model.

Adami shows theoretical example of receptor

specificity (e.g. lock-and-key).

* preexisting redundancy, lack of specificity during evolution can

lead to functionally integrated traits/systems.

For next time, read Adami paper “Reducible Complexity” (Science, 2006):

Similar perspectives:

Lenski et.al "The evolutionary origin of complex features". Nature, 423, 139 (2003).

Clements et.al "The reducible complexity of a mitochondrial molecular machine". PNAS, 106(37), 15791 (2009).

Avida-ED Demonstration

http://avida-ed.msu.edu/

Organismal Self-organization II

* physiomics and scales of life (combining methods vertically- genes to organism) * modularity and evolution (functional subdivisions of life) * specific mechanisms and predictions (epigenetics, phenotypic capacitance, and facilitated variation).

Physiomics and Scales of Life

What is the Physiome? IUPS Physiome http://physiome.nz

Biocomplexity is organized vertically (from genes to morphology and behavior).

The physiome project is a means to better get at connection between these scales in a

computationally rigorous manner.

Model of the heart might combine:

* MRI imaging

* gene expression studies

* finite element analysis (FEA)

* information ontology

Physiomics and Scales of Life (con’t)

Cascade sampling (Weibel, Symmorphoses,

2000):

* recursively subsample tissues and their

component parts.

* in sample, muscles are composed of fibers,

which are composed of mitochondria, and so

on.

Principle: many membranes make up a single

mitochondria, many mitochondria make up

single fiber.

* form a hierarchical relationship, units at one

level contribute to structure and function

at a higher levels in different ways.

Physiomics and Scales of Life (con’t)

NSR Physiome project:

http://nsr.bioeng.washington.edu/

Stated goals of Physiome project:

* parameter sets for different cells,

tissues, and species.

* schema of interactions and types

of relationships.

* databases and models (model

archive).

* definitions of model standards and terminology.

Modularity and Evolution Modularity: the compartmentalization of biological structure and function (within and

across scales).

Why is it important?

Modularity of the organism (body plan) allows for parts

to evolve in parallel or be

conserved independently.

Example: Hox cluster in

Drosophila:

Insect body has segments,

each segment determined

by a Hox gene.

Hox gene family members

are tightly linked and

conserved (no mutation).

Modularity and Evolution (con’t)

Bithorax mutant: a case where Hox gene is not conserved (deleterious).

* in genotype, linkage of Hox genes is broken.

* in phenotype, two sets of wings are expressed.

* in this case, fitness would be severely reduced.

Vertebrate spinal cord: a case where segments can evolve independently.

* variable across a phylogeny (see right).

* different types of vertebrae occur in different numbers

(thoracic, lumbar, sacral, caudal)

* Hox10 triple mutant: lumbar ribs are expressed in mutant,

not in wild-type, mimics what occurs in evolution.

Modularity and Evolution (con’t) So far in this section:

* physiomics, scales of life (organized vertically – cells vs. tissues vs. organs).

* modularity (functional subdivisions of phenomena at one or many scales).

Put relationship between genotype and phenotype in context:

* organization of biocomplexity (skeleton), dynamics of biocomplexity.

* dynamics of biocomplexity = most interesting aspects of evolutionary systems.

Will lead us to:

* specific mechanisms (predictions?) and their theoretical implications.

* epigenetics, phenotypic capacitance, and facilitated variation.

Epigenetics and Phenotypic Capacitance

Epigenetics: the role of chromatin, methylation, and other

non-gene sequence mechanisms (responsible for specific

phenotypic outcomes – see table).

Methylation: addition of a methyl group at C-to-G

transitions in genome. Affects transcription (from DNA

to RNA, protein).

Chromatin state (figure): silent vs. transcriptionally

competent. Chromatin comformational state driven by

histones, affects if and how genes are expressed.

Epigenetics and Phenotypic Capacitance (con’t)

Epigenetic control of phenotypic

expression:

* insert IAP element into allele A.

* methylation now required for

normal expression (unmethylated

= deleterious).

Chromatin state diagram:

systems-level = feedback between

mechanisms.

* methylation of histone H3K9 further

suppresses H3K9 deacetylation/

methylation pathway.

* also work for activators.

Epigenetics and Phenotypic Capacitance (con’t)

In humans, a transgenerational

response has been found:

* consequence of nutritional

differences in development.

* sex-linked (differences between

males and females).

* scarcity in T0, obesity in T2;

abundance in T0, slim phenotypes

in T2.

* involve the resetting of methylation

patterns during gametogenesis of T0.

Evolutionary Capacitance Capacitor Example (Bergman and Siegal, 2003, Nature, 424, 549): Hsp90 (heat

shock protein). Active under “normal” environmental conditions.

Activity: “buffers” genotypic variation (multiple genotypes = single phenotype).

Inactivity: triggered by environment stress (overwhelms function, results in diverse

phenotypic effects). Multiple pleiotropic effects.

Drosophila (fruit fly) larvae example:

Buffering in context:

Left: larvae exposed to mild heat shock,

then to severe heat shock (expression).

Right: larvae exposed to severe heat shock

only (no expression).

Inducible tolerance ~ expression of

proteins in Hsp family (protective

function).

Evolutionary Capacitance (con’t) Wildtype (WT) Knockout (KO)

Before Evolution

(network state)

No compensatory

mechanism needed

Compensatory mechanism

due to loss of genes

After Evolution

(expression outcome)

Var. in gene expression =

lower

Var. in gene expression =

higher

Average variance in gene expression is lower for wildtype (WT) than for the knockout

(KO) given environmental noise.

KOs: when arbitrary genes chosen for deletion, the remaining genes in network =

increased variance in their expression. Leads to increased phenotypic variance.

In silico Evolution (remove buffering,

reset gene network):

KO populations = 314 generations

to new phenotypic optimum (N = 500).

WT populations = 391 generations

to a new phenotypic optimum (N = 500).

Evolutionary Capacitance (con’t) One take-home message:

Accumulation of variation (random mutation,

etc.) during phenotypic buffering ~ Loss-of-

function mutants with higher fitness.

At left:

Power-law distribution of distance (from

buffered phenotypes), very few viable

individuals at large phenotypic distance (see

‘lethal’ category).

Facilitated Variation (FV) and Evolution of Development (Evo-Devo): Parter et.al,

2008, PLoS Computatation Biology, Gerhart and Kirschner, 2006, PNAS USA.

Prediction of FV: evolution is directed by interaction between the demands of

environment and genetic mechanisms (existing variation can guide evolution).

Prediction of Evo-Devo: development “recapitulates” evolution, changes get there as

genes are expressed (gene action).

Facilitated Variation (FV) Review Facilitated variation (FV) can be described mathematically as

FV = (Mn/Ml) * (Dn/Dl)

* Mn are the number of non-lethal mutants.

* Ml is the number of lethal mutants.

* Dn is the phenotypic distance of non-lethal

mutants from the wildtype.

* Dl is the phenotypic distance of lethal mutants

from the wildtype.

As variation is facilitated, both Mn and Dn

become large.

* non-wildtype variants become more abundant and more dissimilar as FV is

maximized.

Facilitated Variation (FV) Review (con’t) Features of Facilitated Variation (comparable with Evo-Devo):

1) weak regulatory linkage: linkage = co-

expression, co-evolution of genes. Linkage

relaxed, relationships less predictable.

2) exploratory behavior: change in effects

of gene, hormonal effects on target tissues.

Genes more diverse in effects.

3) reduced pleiotropy: single gene =

multiple effects (pleiotropy). Genes =

more specific in effects on trait X.

4) modularity: segmental organization,

functional subsets (phenotypic) defined

by distinct gene networks, patterns of

gene expression.

Pleiotropy in

human system

Example of

Phenotypic

Modularity

If you have any further questions/want to

know more:

Bradly Alicea

freejumper@yahoo.com

Research Website

http://www.msu.edu/~aliceabr/